class_docstr: (Dilated) depthwise separable conv.
  ReLU - (Dilated) depthwise separable - Pointwise - BN.
  If dilation == 2, 3x3 conv => 5x5 receptive field, 5x5 conv => 9x9 receptive field.
code: def __init__(
      self,
      C_in,
      C_out,
      kernel_size,
      stride,
      padding,
      dilation,
      affine=True,
  ):
      super().__init__()
      self.net = nn.Sequential(
          nn.ReLU(),
          nn.Conv2d(
              C_in,
              C_in,
              kernel_size,
              stride,
              padding,
              dilation=dilation,
              groups=C_in,
              bias=False,
          ),
          nn.Conv2d(C_in, C_out, 1, stride=1, padding=0, bias=False),
          nn.BatchNorm2d(C_out, affine=affine),
      )
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new DilConv object from inherited base classes: nn with example: obj = DilConv(C_in, C_out, kernel_size, stride, padding, dilation, affine)